1. Field of the Invention
The present innovation relates to procedure for the detection of stress state, wherein ambulatory heart period is measured and the derived signal is segmented into physiological states. The term “stress state” means herein also its opposite, “relaxation state”.
2. Description of the Prior Art
Heart period is among the most commonly used parameters in physiological monitoring. The wide use of heart period is related, on the one hand, to the availability of electrocardiograph (ECG) acquisition device for noninvasive monitoring and, on the other hand, to central role of heart period in the autonomic nervous system function and sensitivity to several physiological states and conditions. Heart period (or, its reciprocal heart rate) forms a basis for different types of analyses and may be defined as the series of intervals between consecutive QRS-waveforms in the ECG-signal. Another method of deriving information on the time distance between consecutive heart beats is the detection of heart beat intervals from heart pulse signal.
The fact that heart period is a complex product of several physiological mechanisms poses a challenge to the use of heart period in applied contexts. This is especially the case within ambulatory measurement, that is, measurement that is performed within natural, free-living condition or field tests, outside of controlled laboratory environment and protocols. However, the multidetermined nature of the heart period may also potentate a derivation of additional physiological measures from the heart period signal by means of decomposing a series of heart periods into separate components that have a physiological interpretation.
It is well known that both branches of the autonomic nervous system (ANS), the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) influence heart rate. It is commonly known that the activity of the SNS and PNS produce, respectively, an increase and decrease in the heart rate level. It is therefore no surprising that much of the work on assessing physiological functions and states using information on heart beat signal often addresses changes in heart beat as stemming from the influence of SNS and/or PNS. Unfortunately, it is usually very difficult to determine precisely the effects of SNS and PNS on the heart rate, since it is often not apparent which branch of the ANS determines changes in heart rate and, in addition to these mechanisms, there are several other mechanisms altering the level of heart rate both directly and indirectly, many of which are not well-known.
The prior art has documented several research lines attempting to use heart rate variability (HRV) to quantify more selectively the activity of SNS and PNS. It has been documented that, especially the power of the so-called high frequency (HF) component of the HRV in the frequency region of 0.15-0.50 Hz provides information on the level of parasympathetic outflow to the heart. Unfortunately, although it has been claimed in some instances that the so-called low-frequency (LF) component of the HRV in the frequency region of 0.04-0.15 reflects SNS activity, the effects of SNS on HRV are rather unclear and it is known that several other mechanisms also influence HRV and especially the LF component, including PNS, hormonal responses, metabolic adjustments, and blood pressure control. Thus, increases and decreases in the level of heart rate and HRV may be due to several sources and therefore, it may be only possible to interpret changes in the heart rate level and heart rate variability as being indicative of the activity level of SNS and PNS during controlled situation and preferably with the aid of other measures.
The concept of stress refers generally in physiological domain to a state of heightened level of physiological activity without immediate or apparent requirements for such arousal. In this document, we use the state of stress to indicate a body balance wherein the overall cardiovascular function, as indicated in, e.g., heart rate and cardiovascular output, is substantially higher than the level that is required by immediate physical metabolic requirements. The physiological state of stress may be due to different sources, such as, for example, physical load (e.g., posture), physical condition (e.g., fewer), mental stress, low level of resources (e.g., a burnout condition), or emotional arousal.
It is of note that the detection of stress relates also closely the metabolic processes such as oxygen consumption and caloric consumption, since cardiovascular output indices alone would falsely indicate that metabolic requirements have increased during a state of stress.
Feedback and information on personal stress state and more generally, resources would be very helpful for many individuals to monitor and manage their stress levels, to avoid a state of burnout, and generally to maintain and enhance health condition. Rest and relaxation are important features of stress management, as they help to reduce stress and further buffer and accumulate resources against the onset and adverse effects of stress.
It has been well-documented in the scientific literature that a state of stress is associated with heightened SNS influence to the heart and lowered or diminished PNS influence to the heart (e.g., Porges 1992). It is also known in the prior art that, at rest during steady conditions, relaxation is shown as lowered level of cardiovascular activity and in specific, a decrease in the level of heart rate, and an increase in the magnitude of the HF component of HRV is often found to associate with state of increased relaxation. Some prior work has been documented to take advantage of the role of HR and HRV in stress and relaxation related phenomena (U.S. Pat. Nos. 4,832,038; 4,862,361; 5,891,044; 5,941,837; 6,104,947; 6,212,427; 6,358,201).
Despite this correlational relationship, there has been not very much progress in the detection of stress-related physiological states on the basis of heart period signal. There has been some prior work on using information on heart period and HRV to classify user states, in particular in combination with other physiological measures such as skin temperature. The prior work based on ECG acquisition has been focused mostly on the determination of clinical condition with using specific autonomic nervous system tests and is therefore very limited in their application to characterize behavioral and physiological states in normal life in connection with, for example, ambulatory measurement (U.S. Pat. Nos. 6,358,201, 5,299,199; 5,419,338; 6,390,986; 6,416,473). For example, the work presented by Childre et al ('201) applies heart rate variability parameters in biofeedback context. The described invention is well-applicable to a controlled situation (e.g., in laboratory, relaxation training) but is clearly not applicable to ambulatory monitoring, wherein it would be crucial to differentiate physical and emotional sources of reactions and responses. Accordingly, if used in ambulatory settings one should use a method of manually selecting time periods of, e.g., relaxation training, to the analysis, as the described work does not include any means of separating different types of physical contexts from each other.
There is also some documented work on the use of ECG and heart period derived measures to detect certain physiological conditions, wherein typically one or more parameters are monitored and a threshold limit is set to signal a change in state (U.S. Pat. Nos. 5,267,568; 6,126,595; 6,358,201). These solutions are necessary limited in the content of classifying states and suffer from the fact that the signal value of the heart period and HRV parameters is not always the same but rather, typically varies in combination with physiological states. In other words, they do not account for the state-varying (conditional) relationships between heart period, HRV parameters, and physiological states.
There has been some work on the modeling of state-varying relationships in physiological signals. However, the prior work is typically not related to the determination of stress, may involve heart rate measurement but require the use of two or more physiological measures (U.S. Pat. Nos. 5,810,014; 5,846,206; 5,902,250; 5,921,937). As an example of the above the work of Davis et al. ('014) presents a general approach for a model-based identification of states according to multichannel measurement of raw bio-signals such as electrocardiographic, electromyographic, and electroencephalographic data. The presented system is geared towards the detection of specific abnormal states from physiological waveforms by using a specific model fitting approach. It is obvious to one skilled in the art that the efficiency of the presented state modelling procedure is highly dependent on the availability of repetitive multichannel data, and thus does not apply for the in-depth analysis and decomposition of one signal, such as heart beat, to differentiate specific body states with characteristic dynamics. Furthermore, it should be clear that the described work is clearly not applicable to the analysis of heart beat signal to differentiate states of emotional stress from other sources of body stress.
It is thus clear that, from the point of differentiating different physiological user states and in comparison to the acquisition of only one signal, these approaches require more effort on the measurement of physiological signals and are therefore susceptible to involving more material costs and more restricted user protocols. More importantly, the referred work does not include any contribution to the identification of stress and relaxation, wherein the occurrence of physical activity has not been able to take account in the context of using heart beat signal as the only single input.
As indicated above, the major problem in the operationalization, measurement and monitoring of stress using information on cardiovascular function, such as acquisition of ECG and heart beat, would be the detection and differentiation of the sources of decreased and increased cardiac function. This is especially evident with increased cardiac activity (e.g., as shown by increased level of heart rate and decreased amplitude of HRV), which may result, for example, from increased state of stress, increased state of physical activity, or postural changes.